Germany: LK Pfaffenhofen a.d.Ilm (Bayern)¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
Notebook executed on: 07/03/2023 11:22:03 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Pfaffenhofen a.d.Ilm", weeks=5);
2023-03-07T11:22:17.294078 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 80 80 100 100 120 120 7-day incidence rate (per 100K people) 72.4 LK Pfaffenhofen a.d.Ilm, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.5 1.0 1.5 2.0 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Pfaffenhofen a.d.Ilm cases daily growth factor Germany-LK Pfaffenhofen a.d.Ilm cases daily growth factor (rolling mean) Germany-LK Pfaffenhofen a.d.Ilm estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Pfaffenhofen a.d.Ilm deaths daily growth factor Germany-LK Pfaffenhofen a.d.Ilm deaths daily growth factor (rolling mean) Germany-LK Pfaffenhofen a.d.Ilm estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 4000 cases doubling time [days] Germany-LK Pfaffenhofen a.d.Ilm doubling time cases (rolling mean) Germany-LK Pfaffenhofen a.d.Ilm doubling time deaths (rolling mean) 0.00 12.98 25.95 38.93 daily change Germany-LK Pfaffenhofen a.d.Ilm new cases (rolling 7d mean) Germany-LK Pfaffenhofen a.d.Ilm new cases 0.000 0.649 1.298 1.947 2.595 daily change Germany-LK Pfaffenhofen a.d.Ilm new deaths (rolling 7d mean) Germany-LK Pfaffenhofen a.d.Ilm new deaths 0.0 86.9 173.8 260.7 347.6 deaths doubling time [days]
In [4]:
overview(country="Germany", subregion="LK Pfaffenhofen a.d.Ilm");
2023-03-07T11:22:33.380055 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 7-day incidence rate (per 100K people) 72.4 LK Pfaffenhofen a.d.Ilm, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2 4 6 8 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Pfaffenhofen a.d.Ilm cases daily growth factor Germany-LK Pfaffenhofen a.d.Ilm cases daily growth factor (rolling mean) Germany-LK Pfaffenhofen a.d.Ilm estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) Germany-LK Pfaffenhofen a.d.Ilm deaths daily growth factor Germany-LK Pfaffenhofen a.d.Ilm deaths daily growth factor (rolling mean) Germany-LK Pfaffenhofen a.d.Ilm estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 8000 cases doubling time [days] Germany-LK Pfaffenhofen a.d.Ilm doubling time cases (rolling mean) Germany-LK Pfaffenhofen a.d.Ilm doubling time deaths (rolling mean) 0.0 259.5 519.1 daily change Germany-LK Pfaffenhofen a.d.Ilm new cases (rolling 7d mean) Germany-LK Pfaffenhofen a.d.Ilm new cases 0.00 2.60 5.19 7.79 10.38 daily change Germany-LK Pfaffenhofen a.d.Ilm new deaths (rolling 7d mean) Germany-LK Pfaffenhofen a.d.Ilm new deaths 0.0 61.7 123.5 185.2 246.9 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Pfaffenhofen a.d.Ilm", dates="2020-03-15:");
2023-03-07T11:25:03.425459 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Pfaffenhofen a.d.Ilm LK Pfaffenhofen a.d.Ilm Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Pfaffenhofen a.d.Ilm Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Pfaffenhofen a.d.Ilm")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Pfaffenhofen a.d.Ilm")
print(f'Population of country="Germany", subregion="LK Pfaffenhofen a.d.Ilm": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table
Population of country="Germany", subregion="LK Pfaffenhofen a.d.Ilm": 129772 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 67073 28 287 0
2023-03-03 67045 11 287 0
2023-03-02 67034 13 287 0
2023-03-01 67021 26 287 0
2023-02-28 66995 16 287 1
2023-02-27 66979 47 286 0
2023-02-24 66932 25 286 1
2023-02-23 66907 23 285 0
2023-02-22 66884 43 285 1
2023-02-21 66841 17 284 0
2023-02-20 66824 45 284 1
2023-02-17 66779 27 283 1
2023-02-16 66752 25 282 0
2023-02-15 66727 21 282 0
2023-02-14 66706 29 282 0
2023-02-13 66677 28 282 0
2023-02-10 66649 17 282 0
2023-02-09 66632 11 282 0
2023-02-08 66621 45 282 1
2023-02-07 66576 29 281 1
2023-02-06 66547 46 280 3
2023-02-03 66501 19 277 0
2023-02-02 66482 28 277 0
2023-02-01 66454 30 277 0
2023-01-31 66424 31 277 0
2023-01-30 66393 27 277 0
2023-01-27 66366 23 277 0
2023-01-26 66343 16 277 0
2023-01-25 66327 17 277 0
2023-01-24 66310 19 277 0
2023-01-23 66291 34 277 0
2023-01-22 66257 1 277 0
2023-01-20 66256 8 277 0
2023-01-19 66248 15 277 0
2023-01-18 66233 17 277 1
2023-01-17 66216 20 276 0
2023-01-16 66196 20 276 2
2023-01-13 66176 13 274 0
2023-01-12 66163 11 274 0
2023-01-11 66152 20 274 0
2023-01-10 66132 15 274 0
2023-01-09 66117 49 274 2
2023-01-05 66068 20 272 0
2023-01-04 66048 34 272 0
2023-01-03 66014 24 272 1
2023-01-02 65990 33 271 1
2022-12-30 65957 24 270 0
2022-12-29 65933 17 270 0
2022-12-28 65916 40 270 0
2022-12-27 65876 61 270 2
2022-12-23 65815 20 268 0
2022-12-22 65795 34 268 0
2022-12-21 65761 32 268 1
2022-12-20 65729 35 267 0
2022-12-19 65694 37 267 0
2022-12-17 65657 1 267 0
2022-12-16 65656 20 267 0
2022-12-15 65636 16 267 1
2022-12-14 65620 26 266 0
2022-12-13 65594 37 266 0
2022-12-12 65557 49 266 2
2022-12-09 65508 19 264 0
2022-12-08 65489 19 264 0
2022-12-07 65470 23 264 0
2022-12-06 65447 31 264 0
2022-12-05 65416 37 264 0
2022-12-02 65379 16 264 0
2022-12-01 65363 27 264 1
2022-11-30 65336 12 263 0
2022-11-29 65324 29 263 0
2022-11-28 65295 24 263 0
2022-11-25 65271 16 263 0
2022-11-24 65255 25 263 0
2022-11-23 65230 16 263 1
2022-11-22 65214 46 262 0
2022-11-21 65168 53 262 1
2022-11-18 65115 13 261 0
2022-11-17 65102 20 261 0
2022-11-16 65082 15 261 0
2022-11-15 65067 50 261 0
2022-11-14 65017 36 261 0
2022-11-11 64981 27 261 0
2022-11-10 64954 19 261 0
2022-11-09 64935 34 261 0
2022-11-08 64901 38 261 0
2022-11-07 64863 66 261 0
2022-11-04 64797 27 261 0
2022-11-03 64770 62 261 0
2022-11-02 64708 48 261 0
2022-10-31 64660 93 261 1
2022-10-29 64567 1 260 0
2022-10-28 64566 41 260 0
2022-10-27 64525 48 260 0
2022-10-26 64477 55 260 0
2022-10-25 64422 95 260 1
2022-10-24 64327 144 259 0
2022-10-22 64183 1 259 0
2022-10-21 64182 70 259 0
2022-10-20 64112 76 259 0
2022-10-19 64036 93 259 0
2022-10-18 63943 152 259 1
2022-10-17 63791 235 258 1
2022-10-14 63556 129 257 0
2022-10-13 63427 166 257 2
2022-10-12 63261 221 255 1
2022-10-11 63040 240 254 0
2022-10-10 62800 379 254 0
2022-10-09 62421 1 254 0
2022-10-08 62420 1 254 0
2022-10-07 62419 176 254 1
2022-10-06 62243 344 253 0
2022-10-05 61899 295 253 0
2022-10-04 61604 363 253 0
2022-10-02 61241 1 253 0
2022-10-01 61240 1 253 0
2022-09-30 61239 179 253 0
2022-09-29 61060 235 253 0
2022-09-28 60825 224 253 0
2022-09-27 60601 209 253 1
2022-09-26 60392 259 252 0
2022-09-23 60133 100 252 0
2022-09-22 60033 108 252 0
2022-09-21 59925 90 252 1
2022-09-20 59835 123 251 0
2022-09-19 59712 171 251 0
2022-09-16 59541 94 251 0
2022-09-15 59447 74 251 0
2022-09-14 59373 106 251 0
2022-09-13 59267 109 251 1
2022-09-12 59158 134 250 0
2022-09-09 59024 58 250 0
2022-09-08 58966 86 250 0
2022-09-07 58880 87 250 1
2022-09-06 58793 131 249 0
2022-09-05 58662 163 249 1
2022-09-02 58499 96 248 0
2022-09-01 58403 94 248 0
2022-08-31 58309 80 248 0
2022-08-30 58229 92 248 0
2022-08-29 58137 157 248 0
2022-08-28 57980 2 248 0
2022-08-26 57978 76 248 0
2022-08-25 57902 53 248 0
2022-08-24 57849 73 248 0
2022-08-23 57776 60 248 0
2022-08-22 57716 112 248 1
2022-08-19 57604 67 247 1
2022-08-18 57537 66 246 0
2022-08-17 57471 97 246 2
2022-08-16 57374 117 244 0
2022-08-14 57257 1 244 0
2022-08-12 57256 45 244 0
2022-08-11 57211 74 244 0
2022-08-10 57137 59 244 0
2022-08-09 57078 104 244 0
2022-08-08 56974 160 244 0
2022-08-05 56814 87 244 0
2022-08-04 56727 67 244 0
2022-08-03 56660 99 244 1
2022-08-02 56561 124 243 0
2022-08-01 56437 185 243 0
2022-07-30 56252 1 243 0
2022-07-29 56251 111 243 0
2022-07-28 56140 146 243 0
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2022-07-17 53704 1 240 0
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2022-07-04 51467 329 238 1
2022-07-01 51138 139 237 0
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2022-06-27 50403 251 237 0
2022-06-25 50152 4 237 0
2022-06-24 50148 119 237 0
2022-06-23 50029 138 237 0
2022-06-22 49891 130 237 0
2022-06-21 49761 181 237 0
2022-06-20 49580 250 237 1
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2022-06-13 48998 153 236 0
2022-06-12 48845 6 236 0
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2022-06-09 48753 90 236 1
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Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)

In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")
Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")
Notebook execution took: 0:03:12.239478